Clinical insights on the spasticity-plus syndrome in multiple sclerosis

Marrouni, Kanza Alami; Duquette, Pierre

doi:10.3389/fneur.2022.958665

Front. Neurol.

2022

DOI: 10.3389/fneur.2022.958665

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Clinical insights on the spasticity-plus syndrome in multiple sclerosis

Kanza Alami Marrouni

Pierre Duquette²

Abstract: The first hypothesis introduced by Fernández et al. (2) on the SPS included only spasticity-associated symptoms in MS and based the cluster on symptoms improving with cannabinoids, mainly nabiximols, a balanced combination of tetrahydrocannabinol and cannabidiol (2). When asked about clustering the suggested symptoms in one syndrome-excluding tremor-, a panel of MS expert neurologists in SpainFrontiers in Neurology frontiersin.org

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2024

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Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis

Fernández Fernández,

Costa-Frossard,

Martínez Ginés

et al. 2024

Front. Neurol.

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IntroductionThe Spasticity-Plus Syndrome (SPS) in multiple sclerosis (MS) refers to a combination of spasticity and other signs/symptoms such as spasms, cramps, bladder dysfunction, tremor, sleep disorder, pain, and fatigue. The main purpose is to develop a user-friendly tool that could help neurologists to detect SPS in MS patients as soon as possible.MethodsA survey research based on a conjoint analysis approach was used. An orthogonal factorial design was employed to form 12 patient profiles combining, at random, the eight principal SPS signs/symptoms. Expert neurologists evaluated in a survey and a logistic regression model determined the weight of each SPS sign/symptom, classifying profiles as SPS or not.Results72 neurologists participated in the survey answering the conjoint exercise. Logistic regression results of the survey showed the relative contribution of each sign/symptom to the classification as SPS. Spasticity was the most influential sign, followed by spasms, tremor, cramps, and bladder dysfunction. The goodness of fit of the model was appropriate (AUC = 0.816). Concordance between the experts’ evaluation vs. model estimation showed strong Pearson’s (r = 0.936) and Spearman’s (r = 0.893) correlation coefficients. The application of the algorithm provides with a probability of showing SPS and the following ranges are proposed to interpret the results: high (> 60%), moderate (30–60%), or low (< 30%) probability of SPS.DiscussionThis study offers an algorithmic tool to help healthcare professionals to identify SPS in MS patients. The use of this tool could simplify the management of SPS, reducing side effects related with polypharmacotherapy.

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Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis

Fernández Fernández,

Costa-Frossard,

Martínez Ginés

et al. 2024

Front. Neurol.

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Clinical insights on the spasticity-plus syndrome in multiple sclerosis

Cited by 1 publication

References 21 publications

Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis

Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis

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